AIMC Topic: Klebsiella pneumoniae
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Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning.
Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi
Nov 29, 2024
BACKGROUND: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relati...
Pioneering Klebsiella Pneumoniae Antibiotic Resistance Prediction With Artificial Intelligence-Clinical Decision Support System-Enhanced Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry: Retrospective Study.
Journal of medical Internet research
Nov 7, 2024
BACKGROUND: The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNB), especially Klebsiella pneumoniae (KP), present a critical global health threat highlighted by the World Health Organization, with mortality ra...
Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.
PloS one
Sep 18, 2024
Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medic...
Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis.
NPJ biofilms and microbiomes
Sep 12, 2024
At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) spec...
Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae.
Journal of infection and public health
Sep 10, 2024
BACKGROUND: Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted la...
Anti-Bacterial Agents
Microbial Sensitivity Tests
Drug Resistance, Multiple, Bacterial
Decision Support Systems, Clinical
Humans
Artificial Intelligence
Machine Learning
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Azabicyclo Compounds
Ceftazidime
Klebsiella pneumoniae
Klebsiella Infections
Drug Combinations
VacSol-ML(ESKAPE) Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.
Vaccine
Aug 9, 2024
The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensiv...
Modelling the rapid detection of Carbapenemase-resistant Klebsiella pneumoniae based on machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.
Diagnostic microbiology and infectious disease
Jul 30, 2024
In this study, 80 carbapenem-resistant Klebsiella pneumoniae (CR-KP) and 160 carbapenem-susceptible Klebsiella pneumoniae (CS-KP) strains detected in the clinic were selected and their matrix-assisted laser desorption/ionization time-of-flight mass s...
Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.
Microbial pathogenesis
Jun 6, 2024
Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in human. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat t...
Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase.
Journal of chemical information and modeling
May 10, 2024
The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved...
Machine-learning-based risk assessment tool to rule out empirical use of ESBL-targeted therapy in endemic areas.
The Journal of hospital infection
Apr 27, 2024
BACKGROUND: Antimicrobial stewardship focuses on identifying patients who require extended-spectrum beta-lactamase (ESBL)-targeted therapy. 'Rule-in' tools have been researched extensively in areas of low endemicity; however, such tools are inadequat...
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